README.md

metadata

title: LLM Scope
emoji: 🔭
short_description: Explore the inners of your favorite LLMs
colorFrom: gray
colorTo: blue
sdk: docker
license: mit
tags:
  - deepseek
  - gemma
  - gpt-oss
  - kimi
  - llama
  - llm
  - mistral
  - nemotron
  - qwen
  - transformer
  - trinity
  - visualization

LLM Scope — Explore the inners of your favorite LLMs

Explore the inners of your favorite LLMs. Visualize transformer architectures and parameter counts without downloading weights.

Space: https://huggingface.co/spaces/omarkamali/llm-scope

Contact: omarkamali.com · omneitylabs.com · hf:omarkamali · x.com/omarkamali · linkedin.com/in/omar-kamali

---

About / Model Inspector

A beautiful, interactive treemap visualizer for transformer model architectures. Enter a HuggingFace model ID or upload a config.json to see a proportional visualization of the model's structure - all without downloading weights.

Features

• No Weights Required: Calculates parameter counts mathematically from config dimensions only
• Interactive Treemap: D3.js zoomable treemap with area proportional to parameter count
• Multiple Architectures: Supports LLaMA, Mistral, Mixtral, GPT-2, BERT, T5, Falcon, Gemma, Qwen, and more
• Beautiful Dark Theme: Modern, minimal UI designed for ML/AI workflows
• Drag & Drop Upload: Upload your own config.json files for analysis

Local Development

Prerequisites

• Python 3.9+
• Node.js (for serving frontend during development)

Setup

1. Clone the repository:

git clone https://huggingface.co/omarkamali/llm-scope.git
cd model-inspector

2. Install Python dependencies:

cd backend
pip install -r requirements.txt

3. Run the server:

uvicorn main:app --reload --port 7860

4. Open http://localhost:7860 in your browser

Running with Docker

docker build -t model-inspector .
docker run -p 7860:7860 model-inspector

API Endpoints

POST /api/inspect

Inspect a model by HuggingFace model ID or config object.

Request Body:

{
  "model_id": "qwen/qwen3-4b-instruct-2507"
}

or

{
  "config": { /* config.json contents */ }
}

Response:

{
  "tree": {
    "name": "Qwen Model",
    "type": "model",
    "params": 1234,
    "children": [...]
  },
  "metadata": {
    "model_id": "qwen/qwen3-4b-instruct-2507",
    "model_type": "llama",
    "total_params": 1234,
    "formatted_params": "...",
    "config": {...}
  }
}

POST /api/upload

Upload a config.json file for inspection.

Request: multipart/form-data with file field

GET /api/health

Health check endpoint.

Parameter Calculation

Parameters are calculated mathematically from config dimensions:

Component	Formula
Embedding	vocab_size × hidden_size
Attention Q/K/V	hidden_size × (num_heads × head_dim)
Attention Output	(num_heads × head_dim) × hidden_size
MLP Up	hidden_size × intermediate_size
MLP Gate (SwiGLU)	hidden_size × intermediate_size
MLP Down	intermediate_size × hidden_size
LayerNorm	2 × hidden_size (weight + bias)
RMSNorm	hidden_size (weight only)
LM Head	hidden_size × vocab_size (if not tied)

Tech Stack

• Backend: FastAPI (Python)
• Frontend: Vanilla JS + D3.js
• Deployment: Docker-based HuggingFace Space

License

MIT